HAN [1]} proposed a hierarchical, node-level and semantic-level attention based heterogeneous graph neural network. HAN requires performing random-walks based on meta-paths. HetGNN [2]} proposed a random-walk with restart strategy that first samples heterogeneous neighbor nodes and then aggregates them based on neighbor features with Bi-LSTM. GATNE[3]} utilizes heterogeneous skip-gram objective function to learn node embeddings of attributed multiplex heterogeneous graphs. The training samples for skip-gram are curated by performing random-walks on nodes with same relations.
HGCN [4]} proposed a heterogeneous graph convolutional network that can learn fine-grained relational features of the heterogeneous graph.
Heterogeneous Graph Transformer (HGT) [5]} proposed a scalable model that can handle dynamic heterogeneous graphs. HGT introduced heterogeneous mutual attention and heterogeneous message passing to perform convolutions on heterogeneous graphs. Note that, these heterogeneous graph models are trained on single-machine system and training these models on a distributed setup requires solving many challenges which are discussed in the next section.
